Identifying enterotype in human microbiome by decomposing probabilistic topics into components

Discovering the global structures of microbial community using large-scale metagenomes is a significant challenge in the era of post-genomics. Data-driven methods such as dimension reduction have shown to be useful when they applied on a metagenomics profile matrix which summarize the abundance of functional or taxonomic categorizations in metagenomic samples. Analogously, model-driven method such as probability topic model (PTM) has been used to build a generative model to simulate the generating of a microbial community based on metagenomic profiles. Data-driven methods are direct and simple, they provide intuitive visualization and understanding of metagenomic profiles. Model-driven methods are often complicated but give a generative mechanism of microbial community which is helpful in understanding the generating process of complex microbial ecology. However, results from model-driven methods are usually hard to visualize and there is less an intuitive understanding of them. We developed a new computational framework to incorporate the strength of data-driven methods into model-based methods and applied the framework to discover and interpret enterotype in human microbiome.